Brain proteome-wide association study implicates novel proteins in depression pathogenesis

Identification of novel drug targets for osteoarthritis by integrating genetics and proteomes from blood

BACKGROUND

Osteoarthritis (OA) is a degenerative osteoarticular disease, involving genetic predisposition. How the risk variants confer the risk of OA through their effects on proteins remains largely unknown. Therefore, we aimed to discover new and effective drug targets for OA and its subtypes.

METHODS

A proteome-wide association study (PWAS) was performed based on OA and its subtypes genome-wide association studies (GWAS) summary datasets and the protein quantitative trait loci (pQTL) data. Subsequently, Mendelian randomization (MR) and colocalization analysis was conducted to estimate the associations between protein and OA risk. The replication analysis was performed in an independent dataset of human plasma pQTL data.

RESULTS

The abundance of seven proteins was causally related to OA, two proteins to knee OA and six proteins to hip OA, respectively. We replicated 2 of these proteins using an independent pQTL dataset. With the further support of colocalization, and higher ECM1 level was causally associated with a higher risk of OA and hip OA. Higher PCSK1 level was causally associated with a lower risk of OA. And higher levels of ITIH1, EFEMP1, and ERLEC1 were associated with decreased risk of hip OA.

CONCLUSION

Our study provides new insights into the genetic component of protein abundance in OA and a promising therapeutic target for future drug development.

Omnibus proteome-wide association study identifies 43 risk genes for Alzheimer disease dementia

Transcriptome-wide association study (TWAS) tools have been applied to conduct proteome-wide association studies (PWASs) by integrating proteomics data with genome-wide association study (GWAS) summary data. The genetic effects of PWAS-identified significant genes are potentially mediated through genetically regulated protein abundance, thus informing the underlying disease mechanisms better than GWAS loci. However, existing TWAS/PWAS tools are limited by considering only one statistical model. We propose an omnibus PWAS pipeline to account for multiple statistical models and demonstrate improved performance by simulation and application studies of Alzheimer disease (AD) dementia. We employ the Aggregated Cauchy Association Test to derive omnibus PWAS (PWAS-O) p values from PWAS p values obtained by three existing tools assuming complementary statistical models-TIGAR, PrediXcan, and FUSION. Our simulation studies demonstrated improved power, with well-calibrated type I error, for PWAS-O over all three individual tools. We applied PWAS-O to studying AD dementia with reference proteomic data profiled from dorsolateral prefrontal cortex of postmortem brains from individuals of European ancestry. We identified 43 risk genes, including 5 not identified by previous studies, which are interconnected through a protein-protein interaction network that includes the well-known AD risk genes TOMM40, APOC1, and APOC2. We also validated causal genetic effects mediated through the proteome for 27 (63%) PWAS-O risk genes, providing insights into the underlying biological mechanisms of AD dementia and highlighting promising targets for therapeutic development. PWAS-O can be easily applied to studying other complex diseases.

miR-29a-5p rescues depressive-like behaviors in a CUMS-induced mouse model by facilitating microglia M2-polarization in the prefrontal cortex via TMEM33 suppression

BACKGROUND

Depression accounts for a high proportion of neuropsychiatric disorders and is associated with abnormal states of neurons in specific brain regions. Microglia play a pivotal role in the inflammatory state during depression development; however, the exact mechanism underlying chronic mood states remains unknown. Thus, the present study aimed to determine whether microRNAs (miRNAs) alleviate stress-induced depression-like behavior in mice by regulating the expression levels of their target genes, explore the role of neuroinflammation induced by microglial activation in the pathogenesis and progression of depression, and determine whether the role of the miR-29a-5p/transmembrane protein 33 (TMEM33) axis.

METHODS

In this study, chronic unpredictable mild stress (CUMS) mouse depression model, various behavioral tests, western blotting, dual-luciferase reporter assay, enzyme-linked immunosorbent assay, real-time quantitative reverse transcription PCR, immunofluorescence and lentivirus-mediated gene transfer were used.

RESULTS

After exposure to the CUMS paradigm, miR-29a-5p was significantly down-regulated. This downregulation subsequently promoted the polarization of microglia M1 by upregulating the expression of TMEM33, resulting in enhanced inflammatory chemokines affecting neurons. Conversely, the upregulation of miR-29a-5p within the prefrontal cortex (PFC) suppressed TMEM33 expression, facilitated microglia M2-polarization, and ameliorated depressive-like behavior.

LIMITATIONS

Only rodent models of depression were used, and human samples were not included.

CONCLUSIONS

The results of this study suggest that miR-29a-5p deficits within the PFC mediate microglial anomalies and contribute to depressive-like behaviors. miR-29a-5p and TMEM33 may, therefore, serve as potential therapeutic targets for the treatment of depression.

Unraveling NEK4 as a Potential Drug Target in Schizophrenia and Bipolar I Disorder: A Proteomic and Genomic Approach

BACKGROUND AND HYPOTHESIS

Investigating the shared brain protein and genetic components of schizophrenia (SCZ) and bipolar I disorder (BD-I) presents a unique opportunity to understand the underlying pathophysiological processes and pinpoint potential drug targets.

STUDY DESIGN

To identify overlapping susceptibility brain proteins in SCZ and BD-I, we carried out proteome-wide association studies (PWAS) and Mendelian Randomization (MR) by integrating human brain protein quantitative trait loci with large-scale genome-wide association studies for both disorders. We utilized transcriptome-wide association studies (TWAS) to determine the consistency of mRNA-protein dysregulation in both disorders. We applied pleiotropy-informed conditional false discovery rate (pleioFDR) analysis to identify common risk genetic loci for SCZ and BD-I. Additionally, we performed a cell-type-specific analysis in the human brain to detect risk genes notably enriched in distinct brain cell types. The impact of risk gene overexpression on dendritic arborization and axon length in neurons was also examined.

STUDY RESULTS

Our PWAS identified 42 proteins associated with SCZ and 14 with BD-I, among which NEK4, HARS2, SUGP1, and DUS2 were common to both conditions. TWAS and MR analysis verified the significant risk gene NEK4 for both SCZ and BD-I. PleioFDR analysis further supported genetic risk loci associated with NEK4 for both conditions. The cell-type specificity analysis revealed that NEK4 is expressed on the surface of glutamatergic neurons, and its overexpression enhances dendritic arborization and axon length in cultured primary neurons.

CONCLUSIONS

These findings underscore a shared genetic origin for SCZ and BD-I, offering novel insights for potential therapeutic target identification.

iTRAQ-Based Serum Proteomic Analysis Reveals Multifactorial Cellular Function Impairment and Aggravated Systematic Inflammation in Drug-free Obsessive-Compulsive Disorders

Obsessive-compulsive disorder (OCD) is a debilitating mental disorder with obvious difficulties in treatment. Its pathogenesis has not been fully elucidated. Further understanding of etiology and mechanism needs to be explored further. We employed the isobaric tag for relative and absolute quantitation (iTRAQ)-based proteomic analysis to compare serum proteome profile between OCD patients and healthy controls, in order to find out the possible mechanism of OCD in the downstream biological process. Eighty-one drug-free OCD patients and 78 healthy controls were enrolled. A total of 475 proteins were identified. Totally, 80 proteins with p < 0.05 were selected for gene set enrichment analysis (GSEA), and only those with a fold change ≥1.2 and q value <0.2 between groups were accepted as differentially expressed proteins (DEPs). We observed a significant enrichment of immuno-inflammation-related pathways, along with intriguing expression trends that immuno-inflammation-related proteins were upregulated in GSEA. After that, 2 up-regulated proteins and 13 down-regulated ones were accepted as DEP. According to the available literature, most of the DEPs have not been reported in OCD. These DEPs were enriched in 121 gene ontology (GO) terms, including hepatocyte growth factor receptor activity, angiogenin-PRI complex, and so on. DEPs were enriched in pathways including adherens junction in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Alterations in DEPs including STXBP5L, GRN, and ANG were validated in OCD animal models. Our study suggested that OCD patients manifested multifactorial impairment in neuronal or non-neuronal cellular function under the inflammatory background. Further research employing larger sample sizes, longitudinal design, stratified analysis, and multiomics methodology will be needed. Experiments in laboratories were essential in illuminating the mechanism.

Identifying novel risk genes in intracranial aneurysm by integrating human proteomes and genetics

Genome-wide association studies (GWAS) have become increasingly popular for detecting numerous loci associated with intracranial aneurysm (IA), but how these loci function remains unclear. In this study, we employed an integrative analytical pipeline to efficiently transform genetic associations and identify novel genes for IA. Using multidimensional high-throughput data, we integrated proteome-wide association studies (PWAS), transcriptome-wide association studies (TWAS), Mendelian randomization (MR) and Bayesian co-localization analyses to prioritize genes that can increase IA risk by altering their expression and protein abundances in the brain and blood. Moreover, single-cell RNA sequencing (scRNA-seq) of the circle of Willis was performed to enrich filtered genes in cells, and gene set enrichment analysis (GSEA) was conducted for each gene using bulk RNA-seq data for IA. No significant genes with cis-regulated plasma protein levels were proven to be associated with IA. The protein abundances of five genes in the brain were found to be associated with IA. According to cellular enrichment analysis, these five genes were expressed mainly in the endothelium, fibroblasts and vascular smooth muscle cells. Only three genes, CNNM2, GPRIN3 and UFL1, passed MR and Bayesian co-localization analyses. While UFL1 was not validated in confirmation PWAS as it was not profiled, it was validated in TWAS. GSEA suggested these three genes are associated with the cell cycle. In addition, the protein abundance of CNNM2 was found to be associated with IA rupture (based on PWAS, MR and co-localization analyses). Our findings indicated that CNNM2, GPRIN3 and UFL1 (CNNM2 correlated with IA rupture) are potential IA risk genes that may provide a broad hint for future research on possible mechanisms and therapeutic targets for IA.

Nominating novel proteins for anxiety via integrating human brain proteomes and genome-wide association study

BACKGROUND

The underlying pathogenesis of anxiety remain elusive, making the pinpointing of potential therapeutic and diagnostic biomarkers for anxiety paramount to its efficient treatment.

METHODS

We undertook a proteome-wide association study (PWAS), fusing human brain proteomes from both discovery (ROS/MAP; N = 376) and validation cohorts (Banner; N = 152) with anxiety genome-wide association study (GWAS) summary statistics. Complementing this, we executed transcriptome-wide association studies (TWAS) leveraging human brain transcriptomic data from the Common Mind Consortium (CMC) to discern the confluence of genetic influences spanning both proteomic and transcriptomic levels. We further scrutinized significant genes through a suite of methodologies.

RESULTS

We discerned 14 genes instrumental in the genesis of anxiety through their specific cis-regulated brain protein abundance. Out of these, 6 were corroborated in the confirmatory PWAS, with 4 also showing associations with anxiety via their cis-regulated brain mRNA levels. A heightened confidence level was attributed to 5 genes (RAB27B, CCDC92, BTN2A1, TMEM106B, and DOC2A), taking into account corroborative evidence from both the confirmatory PWAS and TWAS, coupled with insights from mendelian randomization analysis and colocalization evaluations. A majority of the identified genes manifest in brain regions intricately linked to anxiety and predominantly partake in lysosomal metabolic processes.

LIMITATIONS

The limited scope of the brain proteome reference datasets, stemming from a relatively modest sample size, potentially curtails our grasp on the entire gamut of genetic effects.

CONCLUSION

The genes pinpointed in our research present a promising groundwork for crafting therapeutic interventions and diagnostic tools for anxiety.

NMT2 alleviates depression-like behavior in a rat model of chronic unpredictable stress: An integrated proteomic and phosphoproteomic analysis

Proteomics has been widely used to investigate multiple diseases. Combining the analyses of proteomics with phosphoproteomics can be used to further explain the pathological mechanisms of depression. In this study, depression-like behavior was induced in a rat model of chronic unpredictable mild stress (CUMS). We subsequently conducted the sucrose preference test, open field experiment, and forced swimming test to assess depressive-like behavior. Proteomic and phosphoproteomic sequencing of the hippocampal tissues from depressive-like behavior and normal rats were analyzed to identify differentially expressed proteins (DEPs) and differentially phosphorylated proteins (DPPs). Differentially expressed phosphorylated proteins (DEPPs) were obtained by intersecting the DEPs and DPPs, and functional enrichment analysis, as well as ingenuity pathway analysis (IPA), were subsequently performed. The study also investigated correlations among the DEPPs and used qRT-PCR to quantify the expression levels of key genes. Five DEPPs were identified, Gys1, Nmt2, Lrp1, Bin1, and Atp1a1, which were found to activate the synaptogenesis signaling pathway, induce mitochondrial dysfunction, and activate the phosphoinositide biosynthesis and degradation pathways. The qRT-PCR results confirmed the proteomic findings for Gys1, Nmt2, Lrp1, and Atp1a1. Importantly, inhibiting Nmt2 was found to alleviate depression-like behavior and alleviate neuronal apoptosis in the hippocampus of CUMS rats. In conclusion, we identified five DEPPs associated with the synaptogenesis signaling pathway, mitochondrial dysfunction, and phosphoinositide biosynthesis and degradation in depression. Furthermore, NMT2 may be a potential target for the treatment or diagnosis of depression. Our findings provide novel insights into the molecular mechanisms of depression.

Harnessing Artificial Intelligence in Multimodal Omics Data Integration: Paving the Path for the Next Frontier in Precision Medicine

The integration of multiomics data with detailed phenotypic insights from electronic health records marks a paradigm shift in biomedical research, offering unparalleled holistic views into health and disease pathways. This review delineates the current landscape of multimodal omics data integration, emphasizing its transformative potential in generating a comprehensive understanding of complex biological systems. We explore robust methodologies for data integration, ranging from concatenation-based to transformation-based and network-based strategies, designed to harness the intricate nuances of diverse data types. Our discussion extends from incorporating large-scale population biobanks to dissecting high-dimensional omics layers at the single-cell level. The review underscores the emerging role of large language models in artificial intelligence, anticipating their influence as a near-future pivot in data integration approaches. Highlighting both achievements and hurdles, we advocate for a concerted effort toward sophisticated integration models, fortifying the foundation for groundbreaking discoveries in precision medicine.

Transthyretin Orchestrates Vitamin B12-Induced Stress Resilience

BACKGROUND

Chronic stress significantly contributes to mood and anxiety disorders. Previous data suggest a correlative connection between vitamin B12 supplementation, depression, and stress resilience. However, the underlying mechanisms are still poorly understood.

METHODS

Using the chronic variable stress mouse model coupled with RNA sequencing, we identified vitamin B12-induced transcriptional changes related to stress resilience. Using viral-mediated gene transfer and in vivo epigenome editing, we revealed a functional pathway linking vitamin B12, DNA methylation (DNAme), and depression-like symptoms.

RESULTS

We identified Ttr (transthyretin) as a key sex-specific target of vitamin B12 in chronic stress. Accordingly, TTR expression was increased postmortem in the prefrontal cortex of male but not female patients with depression. Virally altered Ttr in the prefrontal cortex functionally contributed to stress- and depression-related behaviors, changes in dendritic spine morphology, and gene expression. In stressed mice, vitamin B12 reduced DNAme in the Ttr promoter region. Importantly, using in vivo epigenome editing to alter DNAme in the brains of living mice for the first time, we established a direct causal link between DNAme and Ttr and stress-associated behaviors.

CONCLUSIONS

Using state-of-the-art techniques, this study uncovered a mechanistic link between vitamin B12 supplementation, Ttr, and markers of chronic stress and depression, encouraging further studies into dietary interventions for mood disorders.

A Single-Nucleus Transcriptome-Wide Association Study Implicates Novel Genes in Depression Pathogenesis

BACKGROUND

Depression, a common psychiatric illness and global public health problem, remains poorly understood across different life stages, which hampers the development of novel treatments.

METHODS

To identify new candidate genes for therapeutic development, we performed differential gene expression analysis of single-nucleus RNA sequencing data from the dorsolateral prefrontal cortex of older adults (n = 424) in relation to antemortem depressive symptoms. Additionally, we integrated genome-wide association study results for depression (n = 500,199) along with genetic tools for inferring the expression of 14,048 unique genes in 7 cell types and 52 cell subtypes to perform a transcriptome-wide association study of depression followed by Mendelian randomization.

RESULTS

Our single-nucleus transcriptome-wide association study analysis identified 68 candidate genes for depression and showed the greatest number being in excitatory and inhibitory neurons. Of the 68 genes, 53 were novel compared to previous studies. Notably, gene expression in different neuronal subtypes had varying effects on depression risk. Traits with high genetic correlations with depression, such as neuroticism, shared more transcriptome-wide association study genes than traits that were not highly correlated with depression. Complementing these analyses, differential gene expression analysis across 52 neocortical cell subtypes showed that genes such as KCNN2, SCAI, WASF3, and SOCS6 were associated with late-life depressive symptoms in specific cell subtypes.

CONCLUSIONS

These 2 sets of analyses illustrate the utility of large single-nucleus RNA sequencing data both to uncover genes whose expression is altered in specific cell subtypes in the context of depressive symptoms and to enhance the interpretation of well-powered genome-wide association studies so that we can prioritize specific susceptibility genes for further analysis and therapeutic development.

Proteome-wide association study using cis and trans variants and applied to blood cell and lipid-related traits in the Women's Health Initiative study

In most Proteome-Wide Association Studies (PWAS), variants near the protein-coding gene (±1 Mb), also known as cis single nucleotide polymorphisms (SNPs), are used to predict protein levels, which are then tested for association with phenotypes. However, proteins can be regulated through variants outside of the cis region. An intermediate GWAS step to identify protein quantitative trait loci (pQTL) allows for the inclusion of trans SNPs outside the cis region in protein-level prediction models. Here, we assess the prediction of 540 proteins in 1002 individuals from the Women's Health Initiative (WHI), split equally into a GWAS set, an elastic net training set, and a testing set. We compared the testing r2 between measured and predicted protein levels using this proposed approach, to the testing r2 using only cis SNPs. The two methods usually resulted in similar testing r2, but some proteins showed a significant increase in testing r2 with our method. For example, for cartilage acidic protein 1, the testing r2 increased from 0.101 to 0.351. We also demonstrate reproducible findings for predicted protein association with lipid and blood cell traits in WHI participants without proteomics data and in UK Biobank utilizing our PWAS weights.

Identifying Novel Proteins for Chronic Pain: Integration of Human Brain Proteomes and Genome-wide Association Data

Numerous genome-wide association studies have identified risk genes for chronic pain, yet the mechanisms by which genetic variants modify susceptibility have remained elusive. We sought to identify key genes modulating chronic pain risk by regulating brain protein expression. We integrated brain proteomic data with the largest genome-wide dataset for multisite chronic pain (N = 387,649) in a proteome-wide association study (PWAS) using discovery and confirmatory proteomic datasets (N = 376 and 152) from the dorsolateral prefrontal cortex. Leveraging summary data-based Mendelian randomization and Bayesian colocalization analysis, we pinpointed potential causal genes, while a transcriptome-wide association study integrating 452 human brain transcriptomes investigated whether cis-effects on protein abundance extended to the transcriptome. Single-cell RNA-sequencing data and single-nucleus transcriptomic data revealed cell-type-specific expression patterns for identified causal genes in the dorsolateral prefrontal cortex and dorsal root ganglia (DRG), complemented by RNA microarray analysis of expression profiles in other pain-related brain regions. Of the 22 genes cis-regulating protein abundance identified by the discovery PWAS, 18 (82%) were deemed causal by summary data-based Mendelian randomization or Bayesian colocalization analysis analyses, with 7 of these 18 genes (39%) replicating in the confirmatory PWAS, including guanosine diphosphate-mannose pyrophosphorylase B, which also associated at the transcriptome level. Several causal genes exhibited selective expression in excitatory and inhibitory neurons, oligodendrocytes, and astrocytes, while most identified genes were expressed across additional pain-related brain regions. This integrative proteogenomic approach identified 18 high-confidence causal genes for chronic pain, regulated by cis-effects on brain protein levels, suggesting promising avenues for treatment research and indicating a contributory role for the DRG. PERSPECTIVE: The current post genome-wide association study analyses identified 18 high-confidence causal genes regulating chronic pain risk via cis-modulation of brain protein abundance, suggesting promising avenues for future chronic pain therapies. Additionally, the significant expression of these genes in the DRG indicated a potential contributory role, warranting further investigation.

Multi-ancestry meta-analysis of tobacco use disorder identifies 461 potential risk genes and reveals associations with multiple health outcomes

Tobacco use disorder (TUD) is the most prevalent substance use disorder in the world. Genetic factors influence smoking behaviours and although strides have been made using genome-wide association studies to identify risk variants, most variants identified have been for nicotine consumption, rather than TUD. Here we leveraged four US biobanks to perform a multi-ancestral meta-analysis of TUD (derived via electronic health records) in 653,790 individuals (495,005 European, 114,420 African American and 44,365 Latin American) and data from UK Biobank (ncombined = 898,680). We identified 88 independent risk loci; integration with functional genomic tools uncovered 461 potential risk genes, primarily expressed in the brain. TUD was genetically correlated with smoking and psychiatric traits from traditionally ascertained cohorts, externalizing behaviours in children and hundreds of medical outcomes, including HIV infection, heart disease and pain. This work furthers our biological understanding of TUD and establishes electronic health records as a source of phenotypic information for studying the genetics of TUD.

xWAS analysis in neuropsychiatric disorders by integrating multi-molecular phenotype quantitative trait loci and GWAS summary data

BACKGROUND

Integrating quantitative trait loci (QTL) data related to molecular phenotypes with genome-wide association study (GWAS) data is an important post-GWAS strategic approach employed to identify disease-associated molecular features. Various types of molecular phenotypes have been investigated in neuropsychiatric disorders. However, these findings pertaining to distinct molecular features are often independent of each other, posing challenges for having an overview of the mapped genes.

METHODS

In this study, we comprehensively summarized published analyses focusing on four types of risk-related molecular features (gene expression, splicing transcriptome, protein abundance, and DNA methylation) across five common neuropsychiatric disorders. Subsequently, we conducted supplementary analyses with the latest GWAS dataset and corresponding deficient molecular phenotypes using Functional Summary-based Imputation (FUSION) and summary data-based Mendelian randomization (SMR). Based on the curated and supplemented results, novel reliable genes and their functions were explored.

RESULTS

Our findings revealed that eQTL exhibited superior ability in prioritizing risk genes compared to the other QTL, followed by sQTL. Approximately half of the genes associated with splicing transcriptome, protein abundance, and DNA methylation were successfully replicated by eQTL-associated genes across all five disorders. Furthermore, we identified 436 novel reliable genes, which enriched in pathways related with neurotransmitter transportation such as synaptic, dendrite, vesicles, axon along with correlations with other neuropsychiatric disorders. Finally, we identified ten multiple molecular involved regulation patterns (MMRP), which may provide valuable insights into understanding the contribution of molecular regulation network targeting these disease-associated genes.

CONCLUSIONS

The analyses prioritized novel and reliable gene sets related with five molecular features based on published and supplementary results for five common neuropsychiatric disorders, which were missed in the original GWAS analysis. Besides, the involved MMRP behind these genes could be given priority for further investigation to elucidate the pathogenic molecular mechanisms underlying neuropsychiatric disorders in future studies.

A novel multi-omics mendelian randomization method for gene set enrichment and its application to psychiatric disorders

Genome-wide association studies (GWAS) of psychiatric disorders (PD) yield numerous loci with significant signals, but often do not implicate specific genes. Because GWAS risk loci are enriched in expression/protein/methylation quantitative loci (e/p/mQTL, hereafter xQTL), transcriptome/proteome/methylome-wide association studies (T/P/MWAS, hereafter XWAS) that integrate xQTL and GWAS information, can link GWAS signals to effects on specific genes. To further increase detection power, gene signals are aggregated within relevant gene sets (GS) by performing gene set enrichment (GSE) analyses. Often GSE methods test for enrichment of "signal" genes in curated GS while overlooking their linkage disequilibrium (LD) structure, allowing for the possibility of increased false positive rates. Moreover, no GSE tool uses xQTL information to perform mendelian randomization (MR) analysis. To make causal inference on association between PD and GS, we develop a novel MR GSE (MR-GSE) procedure. First, we generate a "synthetic" GWAS for each MSigDB GS by aggregating summary statistics for x-level (mRNA, protein or DNA methylation (DNAm) levels) from the largest xQTL studies available) of genes in a GS. Second, we use synthetic GS GWAS as exposure in a generalized summary-data-based-MR analysis of complex trait outcomes. We applied MR-GSE to GWAS of nine important PD. When applied to the underpowered opioid use disorder GWAS, none of the four analyses yielded any signals, which suggests a good control of false positive rates. For other PD, MR-GSE greatly increased the detection of GO terms signals (2,594) when compared to the commonly used (non-MR) GSE method (286). Some of the findings might be easier to adapt for treatment, e.g., our analyses suggest modest positive effects for supplementation with certain vitamins and/or omega-3 for schizophrenia, bipolar and major depression disorder patients. Similar to other MR methods, when applying MR-GSE researchers should be mindful of the confounding effects of horizontal pleiotropy on statistical inference.

Investigating the Mechanism of Chufan Yishen Formula in Treating Depression through Network Pharmacology and Experimental Verification

Objective: To investigate the antidepressant effect and potential mechanism of the Chufan Yishen Formula (CFYS) through network pharmacology, molecular docking, and experimental verification. Methods: The active ingredients and their target genes of CFYS were identified through Traditional Chinese Medicine Systems Pharmacology (TCMSP) and TCM-ID. We obtained the differentially expressed genes in patients with depression from the GEO database and screened out the genes intersecting with the target genes of CFYS to construct the PPI network. The key pathways were selected through STRING and KEGG. Then, molecular docking and experimental verification were performed. Results: A total of 113 effective components and 195 target genes were obtained. After intersecting the target genes with the differentially expressed genes in patients with depression, we obtained 37 differential target genes, among which HMOX1, VEGFA, etc., were the key genes. After enriching the differential target genes by KEGG, we found that the "chemical carcinogenesis-reactive oxygen species" pathway was the key pathway for the CFYS antidepressant effect. Besides, VEGFA might be a key marker for depression. Experimental verification found that CFYS could significantly improve the behavioral indicators of rats with depression models, including improving the antioxidant enzyme activity and increasing VEGFA levels. The results are consistent with the network pharmacology analysis. Conclusions: CFYS treatment for depression is a multicomponent, multitarget, and multipathway complex process, which may mainly exert an antidepressant effect by improving the neuron antioxidant stress response and regulating VEGFA levels.

Genome-wide association study identifies 30 obsessive-compulsive disorder associated loci

Obsessive-compulsive disorder (OCD) affects ~1% of the population and exhibits a high SNP-heritability, yet previous genome-wide association studies (GWAS) have provided limited information on the genetic etiology and underlying biological mechanisms of the disorder. We conducted a GWAS meta-analysis combining 53,660 OCD cases and 2,044,417 controls from 28 European-ancestry cohorts revealing 30 independent genome-wide significant SNPs and a SNP-based heritability of 6.7%. Separate GWAS for clinical, biobank, comorbid, and self-report sub-groups found no evidence of sample ascertainment impacting our results. Functional and positional QTL gene-based approaches identified 249 significant candidate risk genes for OCD, of which 25 were identified as putatively causal, highlighting WDR6, DALRD3, CTNND1 and genes in the MHC region. Tissue and single-cell enrichment analyses highlighted hippocampal and cortical excitatory neurons, along with D1- and D2-type dopamine receptor-containing medium spiny neurons, as playing a role in OCD risk. OCD displayed significant genetic correlations with 65 out of 112 examined phenotypes. Notably, it showed positive genetic correlations with all included psychiatric phenotypes, in particular anxiety, depression, anorexia nervosa, and Tourette syndrome, and negative correlations with a subset of the included autoimmune disorders, educational attainment, and body mass index.. This study marks a significant step toward unraveling its genetic landscape and advances understanding of OCD genetics, providing a foundation for future interventions to address this debilitating disorder.

Genome-wide meta-analysis, functional genomics and integrative analyses implicate new risk genes and therapeutic targets for anxiety disorders

Anxiety disorders are the most prevalent mental disorders. However, the genetic etiology of anxiety disorders remains largely unknown. Here we conducted a genome-wide meta-analysis on anxiety disorders by including 74,973 (28,392 proxy) cases and 400,243 (146,771 proxy) controls. We identified 14 risk loci, including 10 new associations near CNTNAP5, MAP2, RAB9BP1, BTN1A1, PRR16, PCLO, PTPRD, FARP1, CDH2 and RAB27B. Functional genomics and fine-mapping pinpointed the potential causal variants, and expression quantitative trait loci analysis revealed the potential target genes regulated by the risk variants. Integrative analyses, including transcriptome-wide association study, proteome-wide association study and colocalization analyses, prioritized potential causal genes (including CTNND1 and RAB27B). Evidence from multiple analyses revealed possibly causal genes, including RAB27B, BTN3A2, PCLO and CTNND1. Finally, we showed that Ctnnd1 knockdown affected dendritic spine density and resulted in anxiety-like behaviours in mice, revealing the potential role of CTNND1 in anxiety disorders. Our study identified new risk loci, potential causal variants and genes for anxiety disorders, providing insights into the genetic architecture of anxiety disorders and potential therapeutic targets.

Transcriptome-Wide Association Studies (TWAS): Methodologies, Applications, and Challenges

Transcriptome-wide association study (TWAS) methodologies aim to identify genetic effects on phenotypes through the mediation of gene transcription. In TWAS, in silico models of gene expression are trained as functions of genetic variants and then applied to genome-wide association study (GWAS) data. This post-GWAS analysis identifies gene-trait associations with high interpretability, enabling follow-up functional genomics studies and the development of genetics-anchored resources. We provide an overview of commonly used TWAS approaches, their advantages and limitations, and some widely used applications. © 2024 Wiley Periodicals LLC.

Identification of novel proteins for sleep apnea by integrating genome-wide association data and human brain proteomes

BACKGROUND

Sleep apnea is regarded as a significant global public health issue. The relationship between sleep apnea and nervous system diseases is intricate, yet the precise mechanism remains unclear.

METHODS

In this study, we conducted a comprehensive analysis integrating the human brain proteome and transcriptome with sleep apnea genome-wide association study (GWAS), employing genome-wide association study (PWAS), transcriptome-wide association study (TWAS), Mendelian randomization (MR), and colocalization analysis to identify brain proteins associated with sleep apnea.

RESULTS

The discovery PWAS identified six genes (CNNM2, XRCC6, C3orf18, CSDC2, SQRDL, and DGUOK) whose altered protein abundances in the brain were found to be associated with sleep apnea. The independent confirmatory PWAS successfully replicated four out of these six genes (CNNM2, C3orf18, CSDC2, and SQRDL). The transcriptome level TWAS analysis further confirmed two out of the four genes (C3orf18 and CSDC2). The subsequent two-sample Mendelian randomization provided compelling causal evidence supporting the association of C3orf18, CSDC2, CNNM2, and SQRDL with sleep apnea. The co-localization analysis further supported the association between CSDC2 and sleep apnea (posterior probability of hypothesis 4 = 0.75).

CONCLUSIONS

In summary, the integration of brain proteomic and transcriptomic data provided multifaceted evidence supporting causal relationships between four specific brain proteins (CSDC2, C3orf18, CNNM2, and SQRDL) and sleep apnea. Our findings provide new insights into the molecular basis of sleep apnea in the brain, promising to advance understanding of its pathogenesis in future research.

Elucidating the susceptibility to breast cancer: an in-depth proteomic and transcriptomic investigation into novel potential plasma protein biomarkers

Objectives: This study aimed to identify plasma proteins that are associated with and causative of breast cancer through Proteome and Transcriptome-wide association studies combining Mendelian Randomization. Methods: Utilizing high-throughput datasets, we designed a two-phase analytical framework aimed at identifying novel plasma proteins that are both associated with and causative of breast cancer. Initially, we conducted Proteome/Transcriptome-wide association studies (P/TWAS) to identify plasma proteins with significant associations. Subsequently, Mendelian Randomization was employed to ascertain the causation. The validity and robustness of our findings were further reinforced through external validation and various sensitivity analyses, including Bayesian colocalization, Steiger filtering, heterogeneity and pleiotropy. Additionally, we performed functional enrichment analysis of the identified proteins to better understand their roles in breast cancer and to assess their potential as druggable targets. Results: We identified 5 plasma proteins demonstrating strong associations and causative links with breast cancer. Specifically, PEX14 (OR = 1.201, p = 0.016) and CTSF (OR = 1.114, p < 0.001) both displayed positive and causal association with breast cancer. In contrast, SNUPN (OR = 0.905, p < 0.001), CSK (OR = 0.962, p = 0.038), and PARK7 (OR = 0.954, p < 0.001) were negatively associated with the disease. For the ER-positive subtype, 3 plasma proteins were identified, with CSK and CTSF exhibiting consistent trends, while GDI2 (OR = 0.920, p < 0.001) was distinct to this subtype. In ER-negative subtype, PEX14 (OR = 1.645, p < 0.001) stood out as the sole protein, even showing a stronger causal effect compared to breast cancer. These associations were robustly supported by colocalization and sensitivity analyses. Conclusion: Integrating multiple data dimensions, our study successfully pinpointed plasma proteins significantly associated with and causative of breast cancer, offering valuable insights for future research and potential new biomarkers and therapeutic targets.

Decreased CNNM2 expression in prefrontal cortex affects sensorimotor gating function, cognition, dendritic spine morphogenesis and risk of schizophrenia

Genome-wide association studies (GWASs) have reported multiple single nucleotide polymorphisms (SNPs) associated with schizophrenia, yet the underlying molecular mechanisms are largely unknown. In this study, we aimed to identify schizophrenia relevant genes showing alterations in mRNA and protein expression associated with risk SNPs at the 10q24.32-33 GWAS locus. We carried out the quantitative trait loci (QTL) and summary data-based Mendelian randomization (SMR) analyses, using the PsychENCODE dorsolateral prefrontal cortex (DLPFC) expression QTL (eQTL) database, as well as the ROSMAP and Banner DLPFC protein QTL (pQTL) datasets. The gene CNNM2 (encoding a magnesium transporter) at 10q24.32-33 was identified to be a robust schizophrenia risk gene, and was highly expressed in human neurons according to single cell RNA-seq (scRNA-seq) data. We further revealed that reduced Cnnm2 in the mPFC of mice led to impaired cognition and compromised sensorimotor gating function, and decreased Cnnm2 in primary cortical neurons altered dendritic spine morphogenesis, confirming the link between CNNM2 and endophenotypes of schizophrenia. Proteomics analyses showed that reduced Cnnm2 level changed expression of proteins associated with neuronal structure and function. Together, these results identify a robust gene in the pathogenesis of schizophrenia.

Rosmarinic acid alleviate CORT-induced depressive-like behavior by promoting neurogenesis and regulating BDNF/TrkB/PI3K signaling axis

Rosmarinic acid (RA), a natural phenolic acid compound with a variety of bioactive properties. However, the antidepressant activity and mechanism of RA remain unclear. The aim of this study is to investigate the effects and potential mechanisms of RA on chronic CORT injection induced depression-like behavior in mice. Male C57BL/6 J mice were intraperitoneally injected with CORT (10 mg/kg) and were orally given RA daily (10 or 20 mg/kg) for 21 consecutive days. In vitro, the HT22 cells were exposed to CORT (200 μM) with RA (12.5, 25 or 50 μM) and LY294002 (a PI3K inhibitor) or ANA-12 (a TrkB inhibitor) treatment. The depression-like behavior and various neurobiological changes in the mice and cell injury and levels of target proteins in vitro were subsequently assessed. Here, RA treatment decreased the expression of p-GR/GR, HSP90, FKBP51, SGK-1 in mice hippocampi. Besides, RA increased the average optical density of Nissl bodies and number of dendritic spines in CA3 region, and enhanced Brdu and DCX expression and synaptic transduction in DG region, as well as up-regulated both the BDNF/TrkB/CREB and PI3K/Akt/mTOR signaling. Moreover, RA reduced structural damage and apoptosis in HT22 cells, increased the differentiation and maturation of them. More importantly, LY294002, but not ANA-12, reversed the effect of RA on GR nuclear translocation. Taken together, RA exerted antidepressant activities by modulating the hippocampal glucocorticoid signaling and hippocampal neurogenesis, which related to the BDNF/TrkB/PI3K signaling axis regulating GR nuclear translocation, provide evidence for the application of RA as a candidate for depression.

Protein-centric omics integration analysis identifies candidate plasma proteins for multiple autoimmune diseases

It remains challenging to translate the findings from genome-wide association studies (GWAS) of autoimmune diseases (AIDs) into interventional targets, presumably due to the lack of knowledge on how the GWAS risk variants contribute to AIDs. In addition, current immunomodulatory drugs for AIDs are broad in action rather than disease-specific. We performed a comprehensive protein-centric omics integration analysis to identify AIDs-associated plasma proteins through integrating protein quantitative trait loci datasets of plasma protein (1348 proteins and 7213 individuals) and totally ten large-scale GWAS summary statistics of AIDs under a cutting-edge systematic analytic framework. Specifically, we initially screened out the protein-AID associations using proteome-wide association study (PWAS), followed by enrichment analysis to reveal the underlying biological processes and pathways. Then, we performed both Mendelian randomization (MR) and colocalization analyses to further identify protein-AID pairs with putatively causal relationships. We finally prioritized the potential drug targets for AIDs. A total of 174 protein-AID associations were identified by PWAS. AIDs-associated plasma proteins were significantly enriched in immune-related biological process and pathways, such as inflammatory response (P = 3.96 × 10-10). MR analysis further identified 97 protein-AID pairs with potential causal relationships, among which 21 pairs were highly supported by colocalization analysis (PP.H4 > 0.75), 10 of 21 were the newly discovered pairs and not reported in previous GWAS analyses. Further explorations showed that four proteins (TLR3, FCGR2A, IL23R, TCN1) have corresponding drugs, and 17 proteins have druggability. These findings will help us to further understand the biological mechanism of AIDs and highlight the potential of these proteins to develop as therapeutic targets for AIDs.

Multiomic approach and Mendelian randomization analysis identify causal associations between blood biomarkers and subcortical brain structure volumes

Alterations in subcortical brain structure volumes have been found to be associated with several neurodegenerative and psychiatric disorders. At the same time, genome-wide association studies (GWAS) have identified numerous common variants associated with brain structure. In this study, we integrate these findings, aiming to identify proteins, metabolites, or microbes that have a putative causal association with subcortical brain structure volumes via a two-sample Mendelian randomization approach. This method uses genetic variants as instrument variables to identify potentially causal associations between an exposure and an outcome. The exposure data that we analyzed comprised genetic associations for 2994 plasma proteins, 237 metabolites, and 103 microbial genera. The outcome data included GWAS data for seven subcortical brain structure volumes including accumbens, amygdala, caudate, hippocampus, pallidum, putamen, and thalamus. Eleven proteins and six metabolites were found to have a significant association with subcortical structure volumes, with nine proteins and five metabolites replicated using independent exposure data. We found causal associations between accumbens volume and plasma protease c1 inhibitor as well as strong association between putamen volume and Agouti signaling protein. Among metabolites, urate had the strongest association with thalamic volume. No significant associations were detected between the microbial genera and subcortical brain structure volumes. We also observed significant enrichment for biological processes such as proteolysis, regulation of the endoplasmic reticulum apoptotic signaling pathway, and negative regulation of DNA binding. Our findings provide insights to the mechanisms through which brain volumes may be affected in the pathogenesis of neurodevelopmental and psychiatric disorders and point to potential treatment targets for disorders that are associated with subcortical brain structure volumes.

Proteome-wide mendelian randomization identifies causal plasma proteins in venous thromboembolism development

Genome-wide association studies (GWAS) have identified numerous risk loci for venous thromboembolism (VTE), but it is challenging to decipher the underlying mechanisms. We employed an integrative analytical pipeline to transform genetic associations to identify novel plasma proteins for VTE. Proteome-wide association studies (PWAS) were determined by functional summary-based imputation leveraging data from a genome-wide association analysis (14,429 VTE patients, 267,037 controls), blood proteomes (1348 cases), followed by Mendelian randomization, Bayesian colocalization, protein-protein interaction, and pathway enrichment analysis. Twenty genetically regulated circulating protein abundances (F2, F11, ABO, PLCG2, LRP4, PLEK, KLKB1, PROC, KNG1, THBS2, SERPINA1, RARRES2, CEL, GP6, SERPINE2, SERPINA10, OBP2B, EFEMP1, F5, and MSR1) were associated with VTE. Of these 13 proteins demonstrated Mendelian randomized correlations. Six proteins (F2, F11, PLEK, SERPINA1, RARRES2, and SERPINE2) had strong support in colocalization analysis. Utilizing multidimensional data, this study suggests PLEK, SERPINA1, and SERPINE2 as compelling proteins that may provide key hints for future research and possible diagnostic and therapeutic targets for VTE.

Mendelian randomization studies of depression: evidence, opportunities, and challenges

BACKGROUND

Major depressive disorder (MDD) poses a significant social and economic burden worldwide. Identifying exposures, risk factors, and biological mechanisms that are causally connected to MDD can help build a scientific basis for disease prevention and development of novel therapeutic approaches.

METHODS

In this systematic review, we assessed the evidence for causal relationships between putative causal risk factors and MDD from Mendelian randomization (MR) studies, following PRISMA. We assessed methodological quality based on key elements of the MR design: use of a full instrumental variable analysis and validation of the three key MR assumptions.

RESULTS

We included methodological details and results from 52 articles. A causal link between lifestyle, metabolic, inflammatory biomarkers, particular pathological states and MDD is supported by MR investigations, although results for each category varied substantially.

CONCLUSIONS

While this review shows how MR can offer useful information for examining prospective treatment targets and better understanding the pathophysiology of MDD, some methodological flaws in the existing literature limit reliability of results and probably underlie their heterogeneity. We highlight perspectives and recommendations for future works on MR in psychiatry.

The antidepressant effect of short- and long-term zinc exposition is partly mediated by P2X7 receptors in male mice

Background: As a member of the purinergic receptor family, divalent cation-regulated ionotropic P2X7 (P2rx7) plays a role in the pathophysiology of psychiatric disorders. This study aimed to investigate whether the effects of acute zinc administration and long-term zinc deprivation on depression-like behaviors in mice are mediated by P2X7 receptors. Methods: The antidepressant-like effect of elevated zinc level was studied using a single acute intraperitoneal injection in C57BL6/J wild-type and P2rx7 gene-deficient (P2rx7 -/-) young adult and elderly animals in the tail suspension test (TST) and the forced swim test (FST). In the long-term experiments, depression-like behavior caused by zinc deficiency was investigated with the continuous administration of zinc-reduced and control diets for 8 weeks, followed by the same behavioral tests. The actual change in zinc levels owing to the treatments was examined by assaying serum zinc levels. Changes in monoamine and brain-derived neurotrophic factor (BDNF) levels were measured from the hippocampus and prefrontal cortex brain areas by enzyme-linked immunosorbent assay and high-performance liquid chromatography, respectively. Results: A single acute zinc treatment increased the serum zinc level evoked antidepressant-like effect in both genotypes and age groups, except TST in elderly P2rx7 -/- animals, where no significant effect was detected. Likewise, the pro-depressant effect of zinc deprivation was observed in young adult mice in the FST and TST, which was alleviated in the case of the TST in the absence of functional P2X7 receptors. Among elderly mice, no pro-depressant effect was observed in P2rx7 -/- mice in either tests. Treatment and genotype changes in monoamine and BDNF levels were also detected in the hippocampi. Conclusion: Changes in zinc intake were associated with age-related changes in behavior in the TST and FST. The antidepressant-like effect of zinc is partially mediated by the P2X7 receptor.

Metabolomic Investigation of Major Depressive Disorder Identifies a Potentially Causal Association With Polyunsaturated Fatty Acids

BACKGROUND

Metabolic differences have been reported between individuals with and without major depressive disorder (MDD), but their consistency and causal relevance have been unclear.

METHODS

We conducted a metabolome-wide association study of MDD with 249 metabolomic measures available in the UK Biobank (n = 29,757). We then applied two-sample bidirectional Mendelian randomization and colocalization analysis to identify potentially causal relationships between each metabolite and MDD.

RESULTS

A total of 191 metabolites tested were significantly associated with MDD (false discovery rate-corrected p < .05), which decreased to 129 after adjustment for likely confounders. Lower abundance of omega-3 fatty acid measures and a higher omega-6 to omega-3 ratio showed potentially causal effects on liability to MDD. There was no evidence of a causal effect of MDD on metabolite levels. Furthermore, genetic signals associated with docosahexaenoic acid colocalized with loci associated with MDD within the fatty acid desaturase gene cluster. Post hoc Mendelian randomization of gene-transcript abundance within the fatty acid desaturase cluster demonstrated a potentially causal association with MDD. In contrast, colocalization analysis did not suggest a single causal variant for both transcript abundance and MDD liability, but rather the likely existence of two variants in linkage disequilibrium with one another.

CONCLUSIONS

Our findings suggest that decreased docosahexaenoic acid and increased omega-6 to omega-3 fatty acids ratio may be causally related to MDD. These findings provide further support for the causal involvement of fatty acids in MDD.

Integrating genomics and proteomics data to identify candidate plasma biomarkers for lung cancer risk among European descendants

BACKGROUND

Plasma proteins are potential biomarkers for complex diseases. We aimed to identify plasma protein biomarkers for lung cancer.

METHODS

We investigated genetically predicted plasma levels of 1130 proteins in association with lung cancer risk among 29,266 cases and 56,450 controls of European descent. For proteins significantly associated with lung cancer risk, we evaluated associations of genetically predicted expression of their coding genes with the risk of lung cancer.

RESULTS

Nine proteins were identified with genetically predicted plasma levels significantly associated with overall lung cancer risk at a false discovery rate (FDR) of <0.05. Proteins C2, MICA, AIF1, and CTSH were associated with increased lung cancer risk, while proteins SFTPB, HLA-DQA2, MICB, NRP1, and GMFG were associated with decreased lung cancer risk. Stratified analyses by histological types revealed the cross-subtype consistency of these nine associations and identified an additional protein, ICAM5, significantly associated with lung adenocarcinoma risk (FDR < 0.05). Coding genes of NRP1 and ICAM5 proteins are located at two loci that have never been reported by previous GWAS. Genetically predicted blood levels of genes C2, AIF1, and CTSH were associated with lung cancer risk, in directions consistent with those shown in protein-level analyses.

CONCLUSION

Identification of novel plasma protein biomarkers provided new insights into the biology of lung cancer.

Use of big data and machine learning algorithms to extract possible treatment targets in neurodevelopmental disorders

Neurodevelopmental disorders (NDDs) impact multiple aspects of an individual's functioning, including social interactions, communication, and behaviors. The underlying biological mechanisms of NDDs are not yet fully understood, and pharmacological treatments have been limited in their effectiveness, in part due to the complex nature of these disorders and the heterogeneity of symptoms across individuals. Identifying genetic loci associated with NDDs can help in understanding biological mechanisms and potentially lead to the development of new treatments. However, the polygenic nature of these complex disorders has made identifying new treatment targets from genome-wide association studies (GWAS) challenging. Recent advances in the fields of big data and high-throughput tools have provided radically new insights into the underlying biological mechanism of NDDs. This paper reviews various big data approaches, including classical and more recent techniques like deep learning, which can identify potential treatment targets from GWAS and other omics data, with a particular emphasis on NDDs. We also emphasize the increasing importance of explainable and causal machine learning (ML) methods that can aid in identifying genes, molecular pathways, and more complex biological processes that may be future targets of intervention in these disorders. We conclude that these new developments in genetics and ML hold promise for advancing our understanding of NDDs and identifying novel treatment targets.

Aucubin improves chronic unpredictable mild stress-induced depressive behavior in mice via the GR/NF-κB/NLRP3 axis

Eucommia ulmoides Oliv (EUO) is a traditional therapeutic drug that tonifies the liver and kidney and may improve depression. However, the mechanism of action of the main component, aucubin (AU), is unknown. To study the therapeutic effect of AU, we constructed a chronic unpredictable mild stress (CUMS) depression model in mice. Depression-like behaviors, pathological damage, hormonal changes, inflammation, intranuclear expression of glucocorticoidreceptor (GR), and hippocampal protein expression were assessed. Immunofluorescence staining of the hippocampus showed that CUMS decreased neuronal regeneration, and axons were observed to be reduced and broken. Intracellular GR expression decreased in the hippocampus and hypothalamus, and serum levels of stress hormones increased. Furthermore, molecular changes indicative of pyroptosis were observed. AU administration reversed these changes and significantly improved the depression-like behavior induced by CUMS. Our results suggested that AU improves depression by promoting the intranuclear expression of GR and inhibiting nuclear factor-kappa B-mediated inflammatory activation-driven cell pyroptosis.

Multi-ancestry meta-analysis of tobacco use disorder prioritizes novel candidate risk genes and reveals associations with numerous health outcomes

Tobacco use disorder (TUD) is the most prevalent substance use disorder in the world. Genetic factors influence smoking behaviors, and although strides have been made using genome-wide association studies (GWAS) to identify risk variants, the majority of variants identified have been for nicotine consumption, rather than TUD. We leveraged five biobanks to perform a multi-ancestral meta-analysis of TUD (derived via electronic health records, EHR) in 898,680 individuals (739,895 European, 114,420 African American, 44,365 Latin American). We identified 88 independent risk loci; integration with functional genomic tools uncovered 461 potential risk genes, primarily expressed in the brain. TUD was genetically correlated with smoking and psychiatric traits from traditionally ascertained cohorts, externalizing behaviors in children, and hundreds of medical outcomes, including HIV infection, heart disease, and pain. This work furthers our biological understanding of TUD and establishes EHR as a source of phenotypic information for studying the genetics of TUD.

Identifying Novel Drug Targets for Epilepsy Through a Brain Transcriptome-Wide Association Study and Protein-Wide Association Study with Chemical-Gene-Interaction Analysis

Epilepsy is a severe neurological condition affecting 50-65 million individuals worldwide that can lead to brain damage. Nevertheless, the etiology of epilepsy remains poorly understood. Meta-analyses of genome-wide association studies involving 15,212 epilepsy cases and 29,677 controls of the ILAE Consortium cohort were used to conduct transcriptome-wide association studies (TWAS) and protein-wide association studies (PWAS). Furthermore, a protein-protein interaction (PPI) network was generated using the STRING database, and significant epilepsy-susceptible genes were verified using chip data. Chemical-related gene set enrichment analysis (CGSEA) was performed to determine novel drug targets for epilepsy. TWAS analysis identified 21,170 genes, of which 58 were significant (TWASfdr < 0.05) in ten brain regions, and 16 differentially expressed genes were verified based on mRNA expression profiles. The PWAS identified 2249 genes, of which 2 were significant (PWASfdr < 0.05). Through chemical-gene set enrichment analysis, 287 environmental chemicals associated with epilepsy were identified. We identified five significant genes (WIPF1, IQSEC1, JAM2, ICAM3, and ZNF143) that had causal relationships with epilepsy. CGSEA identified 159 chemicals that were significantly correlated with epilepsy (Pcgsea < 0.05), such as pentobarbital, ketone bodies, and polychlorinated biphenyl. In summary, we performed TWAS, PWAS (for genetic factors), and CGSEA (for environmental factors) analyses and identified several epilepsy-associated genes and chemicals. The results of this study will contribute to our understanding of genetic and environmental factors for epilepsy and may predict novel drug targets.

Proteome-Wide Association Studies for Blood Lipids and Comparison with Transcriptome-Wide Association Studies

Blood lipid traits are treatable and heritable risk factors for heart disease, a leading cause of mortality worldwide. Although genome-wide association studies (GWAS) have discovered hundreds of variants associated with lipids in humans, most of the causal mechanisms of lipids remain unknown. To better understand the biological processes underlying lipid metabolism, we investigated the associations of plasma protein levels with total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL), and low-density lipoprotein cholesterol (LDL) in blood. We trained protein prediction models based on samples in the Multi-Ethnic Study of Atherosclerosis (MESA) and applied them to conduct proteome-wide association studies (PWAS) for lipids using the Global Lipids Genetics Consortium (GLGC) data. Of the 749 proteins tested, 42 were significantly associated with at least one lipid trait. Furthermore, we performed transcriptome-wide association studies (TWAS) for lipids using 9,714 gene expression prediction models trained on samples from peripheral blood mononuclear cells (PBMCs) in MESA and 49 tissues in the Genotype-Tissue Expression (GTEx) project. We found that although PWAS and TWAS can show different directions of associations in an individual gene, 40 out of 49 tissues showed a positive correlation between PWAS and TWAS signed p-values across all the genes, which suggests a high-level consistency between proteome-lipid associations and transcriptome-lipid associations.

Recent advances in the study of sepsis-induced depression

Progress in medicine such as the use of anti-infective drugs and development of the advanced life support equipment has greatly improved the survival rate of patients with sepsis. However, the incidence of sepsis-related diseases is increasing. These include severe neurologic and psychologic disorders, cognitive decline, anxiety, depression, and post-traumatic stress disorder. Cerebral dysfunction occurs via multiple interacting mechanisms, with different causative pathogens having distinct effects. Because sepsis-related diseases place a substantial burden on patients and their families, it is important to elucidate the underlying pathophysiologic mechanisms to develop effective treatments.

Genetic insight into the putative causal proteins and druggable targets of osteoporosis: a large-scale proteome-wide mendelian randomization study

Background: Osteoporosis is a major causative factor of the global burden of disease and disability, characterized by low bone mineral density (BMD) and high risks of fracture. We aimed to identify putative causal proteins and druggable targets of osteoporosis. Methods: This study utilized the largest GWAS summary statistics on plasma proteins and estimated heel BMD (eBMD) to identify causal proteins of osteoporosis by mendelian randomization (MR) analysis. Different GWAS datasets were used to validate the results. Multiple sensitivity analyses were conducted to evaluate the robustness of primary MR findings. We have also performed an enrichment analysis for the identified causal proteins and evaluated their druggability. Results: After Bonferroni correction, 67 proteins were identified to be causally associated with estimated BMD (eBMD) (p < 4 × 10^-5). We further replicated 38 of the 67 proteins to be associated with total body BMD, lumbar spine BMD, femoral neck BMD as well as fractures, such as RSPO3, IDUA, SMOC2, and LRP4. The findings were supported by sensitivity analyses. Enrichment analysis identified multiple Gene Ontology items, including collagen-containing extracellular matrix (GO:0062023, p = 1.6 × 10^-10), collagen binding (GO:0005518, p = 8.6 × 10^-5), and extracellular matrix structural constituent (GO:0005201, p = 2.7 × 10^-5). Conclusion: The study identified novel putative causal proteins for osteoporosis which may serve as potential early screening biomarkers and druggable targets. Furthermore, the role of plasma proteins involved in collagen binding and extracellular matrix in the development of osteoporosis was highlighted. Further studies are warranted to validate our findings and investigate the underlying mechanism.

Integrating GWAS and proteome data to identify novel drug targets for MU

Mouth ulcers have been associated with numerous loci in genome wide association studies (GWAS). Nonetheless, it remains unclear what mechanisms are involved in the pathogenesis of mouth ulcers at these loci, as well as what the most effective ulcer drugs are. Thus, we aimed to screen hub genes responsible for mouth ulcer pathogenesis. We conducted an imputed/in-silico proteome-wide association study to discover candidate genes that impact the development of mouth ulcers and affect the expression and concentration of associated proteins in the bloodstream. The integrative analysis revealed that 35 genes play a significant role in the development of mouth ulcers, both in terms of their protein and transcriptional levels. Following this analysis, the researchers identified 6 key genes, namely BTN3A3, IL12B, BPI, FAM213A, PLXNB2, and IL22RA2, which were related to the onset of mouth ulcers. By combining with multidimensional data, six genes were found to correlate with mouth ulcer pathogenesis, which can be useful for further biological and therapeutic research.

Identifying potential risk genes and pathways for neuropsychiatric and substance use disorders using intermediate molecular mediator information

Neuropsychiatric and substance use disorders (NPSUDs) have a complex etiology that includes environmental and polygenic risk factors with significant cross-trait genetic correlations. Genome-wide association studies (GWAS) of NPSUDs yield numerous association signals. However, for most of these regions, we do not yet have a firm understanding of either the specific risk variants or the effects of these variants. Post-GWAS methods allow researchers to use GWAS summary statistics and molecular mediators (transcript, protein, and methylation abundances) infer the effect of these mediators on risk for disorders. One group of post-GWAS approaches is commonly referred to as transcriptome/proteome/methylome-wide association studies, which are abbreviated as T/P/MWAS (or collectively as XWAS). Since these approaches use biological mediators, the multiple testing burden is reduced to the number of genes (∼20,000) instead of millions of GWAS SNPs, which leads to increased signal detection. In this work, our aim is to uncover likely risk genes for NPSUDs by performing XWAS analyses in two tissues-blood and brain. First, to identify putative causal risk genes, we performed an XWAS using the Summary-data-based Mendelian randomization, which uses GWAS summary statistics, reference xQTL data, and a reference LD panel. Second, given the large comorbidities among NPSUDs and the shared cis-xQTLs between blood and the brain, we improved XWAS signal detection for underpowered analyses by performing joint concordance analyses between XWAS results i) across the two tissues and ii) across NPSUDs. All XWAS signals i) were adjusted for heterogeneity in dependent instruments (HEIDI) (non-causality) p-values and ii) used to test for pathway enrichment. The results suggest that there were widely shared gene/protein signals within the major histocompatibility complex region on chromosome 6 (BTN3A2 and C4A) and elsewhere in the genome (FURIN, NEK4, RERE, and ZDHHC5). The identification of putative molecular genes and pathways underlying risk may offer new targets for therapeutic development. Our study revealed an enrichment of XWAS signals in vitamin D and omega-3 gene sets. So, including vitamin D and omega-3 in treatment plans may have a modest but beneficial effect on patients with bipolar disorder.

Identifying novel genes for amyotrophic lateral sclerosis by integrating human brain proteomes with genome-wide association data

BACKGROUND

Genome-Wide Association Studies (GWAS) have identified numerous risk genes for Amyotrophic Lateral Sclerosis (ALS); however, the mechanisms by which these loci confer ALS risk are uncertain. This study aims to identify novel causal proteins in the brains of patients with ALS using an integrative analytical pipeline.

METHODS

Using the datasets of Protein Quantitative Trait Loci (pQTL) (N_pQTL1 = 376, N_pQTL2 = 152), expression QTL (eQTL) (N = 452), and the largest ALS GWAS (N_ALS=27,205, N_Controls = 110,881), we performed a systematic analytical pipeline including Proteome-Wide Association Study (PWAS), Mendelian Randomization (MR), Bayesian colocalization, and Transcriptome-Wide Association Study (TWAS) to identify novel causal proteins for ALS in the brain.

RESULTS

Using PWAS, we found that the altered protein abundance of 12 genes in the brain was associated with ALS. Three genes (SCFD1, SARM1 and CAMLG) were identified as lead causal genes for ALS with solid evidence (False discovery rate < 0.05, in MR analysis; PPH4 > 80% for Bayesian colocalization). Specifically, an increased abundance of SCFD1 and CAMLG led to an increased risk of ALS, whereas a higher abundance of SARM1 led to a decreased risk of developing ALS. TWAS showed that SCFD1 and CAMLG were related to ALS at the transcriptional level.

CONCLUSIONS

SCFD1, CAMLG, and SARM1 exhibited robust associations and causality with ALS. The study findings provide novel clues for identifying potential therapeutic targets in ALS. Further studies are required to explore the mechanisms underlying the identified genes.

Proteome-wide Mendelian randomization reveals the causal effects of immune-related plasma proteins on psychiatric disorders

Immune dysregulation has been consistently reported in psychiatric disorders, however, the causes and mechanisms underlying immune dysregulation in psychiatric disorders remain largely unclear. Here we conduct a Mendelian randomization study by integrating plasma proteome and GWASs of schizophrenia, bipolar disorder and depression. The primate-specific immune-related protein BTN3A3 showed the most significant associations with all three psychiatric disorders. In addition, other immune-related proteins, including AIF1, FOXO3, IRF3, CFHR4, IGLON5, FKBP2, and PI3, also showed significant associations with psychiatric disorders. Our study showed that a proportion of psychiatric risk variants may contribute to disease risk by regulating immune-related plasma proteins, providing direct evidence that connect the genetic risk of psychiatric disorders to immune system.

Proteome and genome integration analysis of obesity

ABSTRACT

The prevalence of obesity has increased worldwide in recent decades. Genetic factors are now known to play a substantial role in the predisposition to obesity and may contribute up to 70% of the risk for obesity. Technological advancements during the last decades have allowed the identification of many hundreds of genetic markers associated with obesity. However, the transformation of current genetic variant-obesity associations into biological knowledge has been proven challenging. Genomics and proteomics are complementary fields, as proteomics extends functional analyses. Integrating genomic and proteomic data can help to bridge a gap in knowledge regarding genetic variant-obesity associations and to identify new drug targets for the treatment of obesity. We provide an overview of the published papers on the integrated analysis of proteomic and genomic data in obesity and summarize four mainstream strategies: overlap, colocalization, Mendelian randomization, and proteome-wide association studies. The integrated analyses identified many obesity-associated proteins, such as leptin, follistatin, and adenylate cyclase 3. Despite great progress, integrative studies focusing on obesity are still limited. There is an increased demand for large prospective cohort studies to identify and validate findings, and further apply these findings to the prevention, intervention, and treatment of obesity. In addition, we also discuss several other potential integration methods.

Multiomic approach and Mendelian randomization analysis identify causal associations between blood biomarkers and subcortical brain structure volumes

Alterations in subcortical brain structure volumes have been found to be associated with several neurodegenerative and psychiatric disorders. At the same time, genome-wide association studies (GWAS) have identified numerous common variants associated with brain structure. In this study, we integrate these findings, aiming to identify proteins, metabolites, or microbes that have a putative causal association with subcortical brain structure volumes via a two-sample Mendelian randomization approach. This method uses genetic variants as instrument variables to identify potentially causal associations between an exposure and an outcome. The exposure data that we analyzed comprised genetic associations for 2,994 plasma proteins, 237 metabolites, and 103 microbial genera. The outcome data included GWAS data for seven subcortical brain structure volumes including accumbens, amygdala, caudate, hippocampus, pallidum, putamen, and thalamus. Eleven proteins and six metabolites were found to have a significant association with subcortical structure volumes. We found causal associations between amygdala volume and granzyme A as well as association between accumbens volume and plasma protease c1 inhibitor. Among metabolites, urate had the strongest association with thalamic volume. No significant associations were detected between the microbial genera and subcortical brain structure volumes. We also observed significant enrichment for biological processes such as proteolysis, regulation of the endoplasmic reticulum apoptotic signaling pathway, and negative regulation of DNA binding. Our findings provide insights to the mechanisms through which brain volumes may be affected in the pathogenesis of neurodevelopmental and psychiatric disorders and point to potential treatment targets for disorders that are associated with subcortical brain structure volumes.

Astrocytes underlie a faster-onset antidepressant effect of hypidone hydrochloride (YL-0919)

Introduction: Major depression disorder (MDD) is a common and potentially life-threatening mental illness; however, data on its pathogenesis and effective therapeutic measures are lacking. Pathological changes in astrocytes play a pivotal role in MDD. While hypidone hydrochloride (YL-0919), an independently developed antidepressant, has shown rapid action with low side effects, its underlying astrocyte-specific mechanisms remain unclear.

Methods: In our study, mice were exposed to chronic restraint stress (CRS) for 14 days or concomitantly administered YL-0919/fluoxetine. Behavioral tests were applied to evaluate the depression model; immunofluorescence and immunohistochemistry staining were used to explore morphological changes in astrocytes; astrocyte-specific RNA sequencing (RNA-Seq) analysis was performed to capture transcriptome wide alterations; and ATP and oxygen consumption rate (OCR) levels of primary astrocytes were measured, followed by YL-0919 incubation to appraise the alteration of energy metabolism and mitochondrial oxidative phosphorylation (OXPHOS).

Results: YL-0919 alleviated CRS-induced depressive-like behaviors faster than fluoxetine and attenuated the number and morphologic deficits in the astrocytes of depressed mice. The changes of gene expression profile in astrocytes after CRS were partially reversed by YL-0919. Moreover, YL-0919 improved astrocyte energy metabolism and mitochondrial OXPHOS in astrocytes.

Conclusion: Our results provide evidence that YL-0919 exerted a faster-onset antidepressant effect on CRS-mice possibly via astrocyte structural remodeling and mitochondria functional restoration.

A single-nucleus transcriptome-wide association study implicates novel genes in depression pathogenesis

Background

Depression is a common psychiatric illness and global public health problem. However, our limited understanding of the biological basis of depression has hindered the development of novel treatments and interventions.

Methods

To identify new candidate genes for therapeutic development, we examined single-nucleus RNA sequencing (snucRNAseq) data from the dorsolateral prefrontal cortex (N=424) in relation to ante-mortem depressive symptoms. To complement these direct analyses, we also used genome-wide association study (GWAS) results for depression (N=500,199) along with genetic tools for inferring the expression of 22,159 genes in 7 cell types and 55 cell subtypes to perform transcriptome-wide association studies (TWAS) of depression followed by Mendelian randomization (MR).

Results

Our single-nucleus TWAS analysis identified 71 causal genes in depression that have a role in specific neocortical cell subtypes; 59 of 71 genes were novel compared to previous studies. Depression TWAS genes showed a cell type specific pattern, with the greatest enrichment being in both excitatory and inhibitory neurons as well as astrocytes. Gene expression in different neuron subtypes have different directions of effect on depression risk. Compared to lower genetically correlated traits (e.g. body mass index) with depression, higher correlated traits (e.g., neuroticism) have more common TWAS genes with depression. In parallel, we performed differential gene expression analysis in relation to depression in 55 cortical cell subtypes, and we found that genes such as ANKRD36, MADD, TAOK3, SCAI and CHUK are associated with depression in specific cell subtypes.

Conclusions

These two sets of analyses illustrate the utility of large snucRNAseq data to uncover both genes whose expression is altered in specific cell subtypes in the context of depression and to enhance the interpretation of well-powered GWAS so that we can prioritize specific susceptibility genes for further analysis and therapeutic development.

The P2X7 receptor in mood disorders: Emerging target in immunopsychiatry, from bench to bedside

Psychiatric disorders are among the most burdensome disorders worldwide. Though therapies have evolved over the last decades, treatment resistance still affects many patients. Recently, neuroimmune systems have been identified as important factors of mood disorder biology. The underlying dysregulation in neuroimmune cross-talk is driven by genetic risk factors and accumulating adverse environmental influences like chronic psychosocial stress. These result in a cluster of proinflammatory cytokines and quantitative and functional changes of immune cell populations (e.g., microglia, monocytes, T cells), varying by disease entity and state. Among the emerging immune targets, purinergic signalling revolving around the membranous and ATP specific P2X7 receptor (P2X7R) has gained wider attention and clinical studies making use of antagonistic drugs are on-going. Still, no clinically meaningful applications have been identified so far. A major problem is the often overly simplified approach taken to translate findings from bench to bedside. Therefore, the present review focuses on purinergic signalling via P2X7R in the context of recent advances in immunopsychiatric mood disorder research. Our aim is to provide an overview of the current P2X7R-related findings, from bench to bedside. First, we summarize the characteristics of purinergic signalling and P2X7R, followed by a depiction of genetic and clinical data connecting P2X7R to mood disorders. We close with our perspective on current developments and discuss changes necessary to translate the evident potential of P2X7R signalling modulation into meaningful clinical application. This article is part of the Special Issue on 'Purinergic Signaling: 50 years'.

Multi-omics data integration methods and their applications in psychiatric disorders

To study mental illness and health, in the past researchers have often broken down their complexity into individual subsystems (e.g., genomics, transcriptomics, proteomics, clinical data) and explored the components independently. Technological advancements and decreasing costs of high throughput sequencing has led to an unprecedented increase in data generation. Furthermore, over the years it has become increasingly clear that these subsystems do not act in isolation but instead interact with each other to drive mental illness and health. Consequently, individual subsystems are now analysed jointly to promote a holistic understanding of the underlying biological complexity of health and disease. Complementing the increasing data availability, current research is geared towards developing novel methods that can efficiently combine the information rich multi-omics data to discover biologically meaningful biomarkers for diagnosis, treatment, and prognosis. However, clinical translation of the research is still challenging. In this review, we summarise conventional and state-of-the-art statistical and machine learning approaches for discovery of biomarker, diagnosis, as well as outcome and treatment response prediction through integrating multi-omics and clinical data. In addition, we describe the role of biological model systems and in silico multi-omics model designs in clinical translation of psychiatric research from bench to bedside. Finally, we discuss the current challenges and explore the application of multi-omics integration in future psychiatric research. The review provides a structured overview and latest updates in the field of multi-omics in psychiatry.

The Immune System and Depression: From Epidemiological to Clinical Evidence

Depression is a frequent mental disorder with a substantial contribution to years lived with disability worldwide. In the search for new treatment targets, the immune system's contribution to the pathogenesis of depression has received increased attention as immune activation has been associated with depression in various epidemiological and case-control studies. Epidemiological studies have shown that immune exposures such as severe infections and autoimmune disorders increase the risk of depression. Furthermore, immune system activation has been indicated in case-control studies of depression revealing higher levels of key pro-inflammatory cytokines among patients with depression than healthy controls, particularly in blood and to some extent in the cerebrospinal fluid. Moreover, brain imaging studies indicate increased microglial activity during depression, and gut microbiota studies have documented alterations of gut microbiota composition to be associated with depression. Based on findings from animal and human studies, several immune-mediated molecular mechanisms have been suggested to underlie the association between increased immunological activity and depression. However, the research is challenged by the heterogeneity of the depression diagnosis and - to some extent - the precision of currently available technology for immune biomarker quantification, particularly regarding the assessment of low-grade neuroinflammation. Nonetheless, an enhanced understanding of the complex interactions between the immune system and the brain in the context of depression could pave the way for precision medicine approaches with immune-modulating treatment as a promising additional option in the treatment of depression.

Integrative Post-Genome-Wide Association Study Analyses Relevant to Psychiatric Disorders: Imputing Transcriptome and Proteome Signals

Background

The genome-wide association study (GWAS) is a common tool to identify genetic variants associated with complex traits, including psychiatric disorders (PDs). However, post-GWAS analyses are needed to extend the statistical inference to biologically relevant entities, e.g., genes, proteins, and pathways. To achieve this goal, researchers developed methods that incorporate biologically relevant intermediate molecular phenotypes, such as gene expression and protein abundance, which are posited to mediate the variant-trait association. Transcriptome-wide association study (TWAS) and proteome-wide association study (PWAS) are commonly used methods to test the association between these molecular mediators and the trait.

Summary

In this review, we discuss the most recent developments in TWAS and PWAS. These methods integrate existing "omic" information with the GWAS summary statistics for trait(s) of interest. Specifically, they impute transcript/protein data and test the association between imputed gene expression/protein level with phenotype of interest by using (i) GWAS summary statistics and (ii) reference transcriptomic/proteomic/genomic datasets. TWAS and PWAS are suitable as analysis tools for (i) primary association scan and (ii) fine-mapping to identify potentially causal genes for PDs.

Key Messages

As post-GWAS analyses, TWAS and PWAS have the potential to highlight causal genes for PDs. These prioritized genes could indicate targets for the development of novel drug therapies. For researchers attempting such analyses, we recommend Mendelian randomization tools that use GWAS statistics for both trait and reference datasets, e.g., summary Mendelian randomization (SMR). We base our recommendation on (i) being able to use the same tool for both TWAS and PWAS, (ii) not requiring the pre-computed weights (and thus easier to update for larger reference datasets), and (iii) most larger transcriptome reference datasets are publicly available and easy to transform into a compatible format for SMR analysis.